ChiSqSelectorModel

class pyspark.ml.feature.ChiSqSelectorModel(java_model: Optional[JavaObject] = None)

Model fitted by ChiSqSelector.

Methods

clear(param)	Clears a param from the param map if it has been explicitly set.
copy([extra])	Creates a copy of this instance with the same uid and some extra params.
explainParam(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()	Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])	Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
getFdr()	Gets the value of fdr or its default value.
getFeaturesCol()	Gets the value of featuresCol or its default value.
getFpr()	Gets the value of fpr or its default value.
getFwe()	Gets the value of fwe or its default value.
getLabelCol()	Gets the value of labelCol or its default value.
getNumTopFeatures()	Gets the value of numTopFeatures or its default value.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getOutputCol()	Gets the value of outputCol or its default value.
getParam(paramName)	Gets a param by its name.
getPercentile()	Gets the value of percentile or its default value.
getSelectorType()	Gets the value of selectorType or its default value.
hasDefault(param)	Checks whether a param has a default value.
hasParam(paramName)	Tests whether this instance contains a param with a given (string) name.
isDefined(param)	Checks whether a param is explicitly set by user or has a default value.
isSet(param)	Checks whether a param is explicitly set by user.
load(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
read()	Returns an MLReader instance for this class.
save(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)	Sets a parameter in the embedded param map.
setFeaturesCol(value)	Sets the value of featuresCol.
setOutputCol(value)	Sets the value of outputCol.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

fdr
featuresCol
fpr
fwe
labelCol
numTopFeatures
outputCol
params	Returns all params ordered by name.
percentile
selectedFeatures	List of indices to select (filter).
selectorType

Methods Documentation

clear(param: pyspark.ml.param.Param) → None

Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extradict, optional

Extra parameters to copy to the new instance

Returns

JavaParams

Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters

extradict, optional

extra param values

Returns

dict

merged param map

getFdr() → float

Gets the value of fdr or its default value.

getFeaturesCol() → str

Gets the value of featuresCol or its default value.

getFpr() → float

Gets the value of fpr or its default value.

getFwe() → float

Gets the value of fwe or its default value.

getLabelCol() → str

Gets the value of labelCol or its default value.

getNumTopFeatures() → int

Gets the value of numTopFeatures or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getOutputCol() → str

Gets the value of outputCol or its default value.

getParam(paramName: str) → pyspark.ml.param.Param

Gets a param by its name.

getPercentile() → float

Gets the value of percentile or its default value.

getSelectorType() → str

Gets the value of selectorType or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param has a default value.

hasParam(paramName: str) → bool

Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL

Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

Returns an MLReader instance for this class.

save(path: str) → None

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

setOutputCol(value: str) → P

Sets the value of outputCol.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

Transforms the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame

transformed dataset

write() → pyspark.ml.util.JavaMLWriter

Returns an MLWriter instance for this ML instance.

Attributes Documentation

fdr: pyspark.ml.param.Param[float] = Param(parent='undefined', name='fdr', doc='The upper bound of the expected false discovery rate.')

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')

fpr: pyspark.ml.param.Param[float] = Param(parent='undefined', name='fpr', doc='The highest p-value for features to be kept.')

fwe: pyspark.ml.param.Param[float] = Param(parent='undefined', name='fwe', doc='The upper bound of the expected family-wise error rate.')

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')

numTopFeatures: pyspark.ml.param.Param[int] = Param(parent='undefined', name='numTopFeatures', doc='Number of features that selector will select, ordered by ascending p-value. If the number of features is < numTopFeatures, then this will select all features.')

outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')

params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

percentile: pyspark.ml.param.Param[float] = Param(parent='undefined', name='percentile', doc='Percentile of features that selector will select, ordered by ascending p-value.')

selectedFeatures

List of indices to select (filter).

selectorType: pyspark.ml.param.Param[str] = Param(parent='undefined', name='selectorType', doc='The selector type. Supported options: numTopFeatures (default), percentile, fpr, fdr, fwe.')

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